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This section explores the theoretical framework used in this study to identify the relationship between oil consumption and oil price volatility and economic activities in developing economies. For this purpose, this section first presents the supply-side approach of analysing the impact of oil consumption on output, followed by the demand-side approach to further investigate the impact of oil consumption. The section ends with identifying the impact of oil price volatility on economic activities for the concerned countries.

3.2.1 Impact of oil consumption on output: the supply-side approach

The concept of aggregate production function plays a key role in the field of economic studies regarding growth and development. Throughout the neoclassical literature of economic growth the concept has been extensively used to identify the sources of economic development and to assess the contribution of different inputs in accelerating growth. Both the neoclassical production functions introduced by Cobb and Douglas (1928) and further developed by Tinbergen (1942), popularly known as Cobb-Douglas production function, and the function presented by Arrow et al. (1961), subsequently called the Constant Elasticity of Substitution (CES), assume constant returns to scale. Both these production functions have been

extensively investigated in both theoretical and empirical economics literature.

However, the Cobb-Douglas-type production function remains the most popular instrument for finding relationships between energy and economic variables because of two important reasons. One reason is the simplicity of the Cobb-Douglas production function from an estimation point of view. Secondly, it seems to fit with most economic data.

In the first part of the empirical analysis, this thesis examines the relationship between oil consumption and output in a three factor (capital, labour and oil consumption) production function framework. This thesis follows the spirit similar to Ghali and El-Sakka (2004), Soytas and Sari (2006) and Sari and Soytas (2007).

However, this study extends the approach further by analysing the contribution of oil consumption on the total factor productivity of developing economies. The theoretical underpinning is elaborated below.

Weitzman (1970) formalizes the aggregate production function algebraically in a general form as:

) , ( _{t t}

t

t A f K L

Y  (3.1)

where Yt indicates aggregate output at time t, Kt is the flow of services provided by the existing capital stock rather than the capital stock itself, Lt is the labour employed in production, At is the level of technology, which is also the measure of total factor productivity, and f(.) is the function describing the connection between the variables K and L.

The aggregate production function, as presented in equation (3.1) above, commonly used in early empirical works assumes a Cobb-Douglas functional form that has constant returns to scale. Thus, equation (3.1) becomes:

t,

t t t

t AK L

Y  ^{ }  1 (3.2)

where α and β measure the elasticity of output with respect to capital and labour, respectively. Each of the elasticities is assumed to be constant and lying between zero and unity. The parameter A may be regarded as a technology parameter. _t is the stochastic disturbance term.

This thesis investigates the contribution of oil consumption to output of developing economies. With the help of recent developments in time-series econometrics, the supply-side impact of oil consumption (OC) on output will be investigated through the following oil inclusive production function:

t t t t

t AK L OC

Y  ^{  } (3.3)

where γ measure the elasticity of output with respect to oil consumption.

3.2.2 Impact of oil consumption on output: the demand-side approach

Previous literature concerning the demand-side analysis mainly focuses on ascertaining the impact on energy consumption of economic activities. As mentioned earlier, most of these studies adopted a tri-variate framework consisting of energy consumption, income and CPI. Here, CPI is used as a proxy of energy prices. Since this study focuses on the impact on oil consumption, the inclusion of oil consumption in the model opens up a unique opportunity to incorporate the actual price of the commodity, i. e. oil price. Furthermore, demand for oil consumption and output determine the level of pollutant emissions in the environment. Thus, a complete demand-side analysis should take into account this dynamic relationship between oil consumption, output, oil price and CO2 emissions.

This study estimates two different demand-side equations. The first one analyses the relationship between oil consumption and output within the framework presented in the following equation:

OC = F(Y, P) (3.4)

where, OC is oil consumption, Y is output and P is oil prices.

The second equation estimated for the purpose of analysing the dynamic relationship among carbon emissions, output and oil consumption is as follows:

CO2 = F(Y, OC) (3.5)

where CO2 is carbon emission. This study analyses Equation 3.4 and 3.5 to investigate both long- and short-run relationships among output, oil consumption and pollutant emission.

3.2.3 Oil price volatility and the economy

It is now well established in both empirical and theoretical literature that oil price shocks exert adverse impacts on different macroeconomic indicators through raising production and operational costs. Alternatively, large oil price changes, either increases or decreases, i.e. volatility; may affect the economy adversely because they delay business investment by raising uncertainty or by inducing costly sectoral resource reallocation.

Bernanke (1983) offers a theoretical explanation of the uncertainty channel by demonstrating that, when the firms experience increased uncertainty about the future price of oil then it is optimal for them to postpone irreversible investment expenditures. When a firm is confronted with a choice of whether to add energy-efficient or energy-inenergy-efficient capital, increased uncertainty born by oil price volatility raises the option value associated with waiting to invest. As the firm waits for more updated information, it forgoes returns obtained by making an early commitment, but the chances of making the right investment decision increase.

Thus, as the level of oil price volatility increases, the option value rises and the incentive to investment declines (Ferderer 1996). The downward trend in investment incentives ultimately transmits to different sectors of the economy.

Hamilton (1988) discusses the sectoral resource allocation channel. In this study by constructing a multi-sector model, the author demonstrates that relative price shocks can lead to a reduction in aggregate employment by inducing workers of the adversely affected sectors to remain unemployed while waiting for the conditions to improve in their own sector rather than moving to other positively affected sectors.

Lilien (1982) also show that aggregate unemployment rises when relative price shocks becomes more variable.

This study analyses the impact of oil price volatility on developing economies within the following framework:

X = F (OPV) (3.6)

where X is a matrix of two macroeconomic indicators, namely, GDP growth and inflation, on which the impact of oil price volatility is ascertained and OPV is oil price volatility.

Oil price volatility is constructed through the application of realized volatility measures. The method employed in constructing the volatility measure is discussed later in the data section of this thesis.

For the purpose of fulfilling the objectives of this thesis different time-series econometric and panel data analysis techniques are used. The following section discusses all the methods that are employed in the empirical analysis of this study.